Design and Analysis of Learning Classifier Systems : A Probabilistic Approach
This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem sp...
Salvato in:
| Autore principale: | |
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| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edizione: | 1st ed. 2008. |
| Serie: | Studies in Computational Intelligence
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| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Design and Analysis of Learning Classifier Systems, Texte imprimé, 9783540798651 • Design and Analysis of Learning Classifier Systems, Texte imprimé, 9783642098611 • Design and Analysis of Learning Classifier Systems, Texte imprimé, 9783540872771 • Design and Analysis of Learning Classifier Systems, Texte imprimé, 9783540798651 |
| Riassunto: | This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems. Contrary to commonly approaching their design and analysis from the viewpoint of evolutionary computation, this book instead promotes a probabilistic model-based approach, based on their defining question "What is an LCS supposed to learn?". Systematically following this approach, it is shown how generic machine learning methods can be applied to design LCS algorithms from the first principles of their underlying probabilistic model, which is in this book -- for illustrative purposes -- closely related to the currently prominent XCS classifier system. The approach is holistic in the sense that the uniform goal-driven design metaphor essentially covers all aspects of LCS and puts them on a solid foundation, in addition to enabling the transfer of the theoretical foundation of the various applied machine learning methods onto LCS. Thus, it does not only advance the analysis of existing LCS but also puts forward the design of new LCS within that same framework |
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| Descrizione del documento: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783540798668 |
| ISSN: | 1860-9503 |
| Accesso: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |